Skip to content

Latest commit

 

History

History
29 lines (21 loc) · 2.67 KB

PromptInjection.md

File metadata and controls

29 lines (21 loc) · 2.67 KB

Vulnerable Prompt Injection in ML/AI Systems

Description

The vulnerability of ML/AI systems to prompt injection attacks arises when external entities can influence or inject malicious prompts, leading the models to generate unintended or harmful content.

Extended Description

Prompt injection in ML/AI systems can manipulate a model's output. Similar to traditional injection attacks in software systems, attackers can craft specific prompts that, when processed by the model, produce outputs beneficial to the attacker. These might include misleading information, inappropriate content, or outputs that exploit downstream system vulnerabilities.

Potential Mitigations

  • Input Validation: Ensure that all prompts or inputs to the models are strictly validated against a set of accepted values or patterns.
  • Output Sanitization: Even after input validation, sanitize the outputs from the models to remove or neutralize potentially harmful content.
  • Restrict Prompt Flexibility: Use fixed or restricted prompt structures where possible to minimize the risk of malicious inputs.
  • User Role Management: Limit the ability to modify or inject prompts based on user roles, ensuring that only trusted users can influence model inputs.
  • Monitoring and Logging: Continuously monitor model interactions and log all prompt modifications or insertions to trace potential attacks and respond promptly.
  • Escaping Special Characters: Neutralize special characters or sequences that might have special meanings for the ML/AI model.

Related Weaknesses

  • CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection'): This highlights the generic risk of injection attacks, where external inputs can influence system behavior.
  • CWE-20: Improper Input Validation: Underscores the need for stringent validation routines to prevent harmful inputs.

Impact Analysis

  • Misleading Outputs: Systems may produce information that is incorrect or serves the attacker's intentions.
  • System Exploitation: Crafted prompts can cause models to produce outputs that result in exploitable behavior in downstream systems or applications.
  • Reputation Damage: Generating harmful content due to prompt injection can erode user trust and harm the reputation of the ML/AI system or the organization.
  • Legal and Compliance Risks: Producing misleading or harmful content, especially in regulated sectors, might lead to legal consequences or compliance breaches.
  • User Data Risks: If the system processes or acts upon user data based on injected prompts, it may reveal sensitive information or make incorrect decisions based on malicious outputs.